A mobile energy storage photovoltaic consumption method based on Dijkstra algorithm path planning

Abstract

The present invention discloses a mobile energy storage photovoltaic consumption method based on Dijkstra algorithm path planning, which belongs to the field of path planning and includes the following steps: S1: marking all photovoltaic grid connection nodes and the nodes where the mobile energy storage vehicle is located; calculating the minimum traffic-grid coupling coefficient ω ij ; S2: ω ij The node where the smallest mobile energy storage vehicle is located is updated to the set Q; S3: Establish a model for optimizing the scheduling of mobile energy storage vehicles, and screen the mobile energy storage vehicles in the set Q; Establish the objective function of the model for optimizing the scheduling of energy storage vehicles; Constraints include: energy constraints of mobile energy storage vehicles, time and space constraints of mobile energy storage vehicles, and charging and discharging constraints and energy constraints of mobile energy storage vehicles; S4: Bring the mobile energy storage vehicles in the set Q into S3 for calculation, and screen out the mobile energy storage vehicles at the photovoltaic grid-connected point j. This method can reasonably allocate the energy of mobile energy storage vehicles and use mobile energy storage vehicles to absorb photovoltaic power at photovoltaic grid-connected points.

Description

Technical Field

[0001] The present invention belongs to the field of path planning methods, and specifically relates to a mobile energy storage photovoltaic consumption method based on Dijkstra algorithm path planning. Background Art

[0002] Distributed photovoltaics, a representative of the new energy sector, is experiencing rapid growth, and photovoltaic power consumption has become a key issue in the energy storage sector. Mobile energy storage vehicles, as an effective means of photovoltaic power consumption, can be moved between different load units, providing services such as load transfer and voltage regulation.

[0003] However, how to plan the mobile energy storage vehicle's movement path so that it can reach the photovoltaic grid connection point more reasonably; and how to enable the mobile energy storage vehicle to maximize the absorption of the photovoltaic power of the photovoltaic grid connection point; both issues still need to be studied. Summary of the Invention

[0004] The present invention provides a mobile energy storage photovoltaic consumption method based on Dijkstra algorithm path planning, which is used to screen out mobile energy storage vehicles arriving at photovoltaic grid-connected points and utilize the mobile energy storage vehicles to consume photovoltaic power at the photovoltaic grid-connected points.

[0005] In order to achieve the above object, the present invention provides a mobile energy storage photovoltaic consumption method based on Dijkstra algorithm path planning, which is characterized by comprising the following steps:

[0006] S1: Mark all photovoltaic grid-connected nodes and the nodes where mobile energy storage vehicles are located; calculate the minimum traffic-grid coupling coefficient ωi j ;

[0007]

[0008] Where: μ1 and μ2 are weight coefficients; ω T is the travel time of the mobile energy storage vehicle from node i to node j, which is known from real-time traffic information; ω P is the photovoltaic power of the photovoltaic grid-connected point at node j, which is a known quantity;

[0009] S2: ω ij The node where the smallest mobile energy storage vehicle is located is updated to the set Q;

[0010] S3: Establish a model for optimizing the scheduling of mobile energy storage vehicles and screen the mobile energy storage vehicles in the set Q. The objective function of the model for optimizing the scheduling of mobile energy storage vehicles is:

[0011]

[0012] Where: and is the weight coefficient; in is the photovoltaic surplus power at node j at time t; is the photovoltaic power at the photovoltaic grid connection point j of the photovoltaic energy storage vehicle at time t; is the photovoltaic power at the photovoltaic grid-connected point j;

[0013] Constraints include: energy constraints of mobile energy storage vehicles, time and space constraints of mobile energy storage vehicles, and charging and discharging constraints and energy constraints of mobile energy storage vehicles;

[0014] S4: Bring the mobile energy storage vehicles in the set Q into S3 for calculation, and filter out the mobile energy storage vehicles that are connected to the photovoltaic grid point j.

[0015] Furthermore, the energy constraint of the mobile energy storage vehicle is expressed by the following formula:

[0016]

[0017]

[0018]

[0019]

[0020]

[0021]

[0022]

[0023] E min ≤E j.t ≤E max (10)

[0024] Where: j is the photovoltaic grid connection point and the node where each level of load is located; and are the charging and discharging active powers of the mobile energy storage vehicle at node j at time t; and are the reactive powers of charging and discharging of the mobile energy storage vehicle at node j at time t; is the maximum apparent power of the energy storage converter of the mth mobile energy storage vehicle; and are the charging and discharging flags of the mobile energy storage vehicle at node j at time t. If the mobile energy storage is in the charging state, then Otherwise 0; p chmax and p dismax are the maximum charging power and discharging power of the mobile energy storage vehicle respectively; and are the charging efficiency and discharging efficiency of the mobile energy storage vehicle at node j respectively; E j,t represents the amount of electricity of the mobile energy storage vehicle at node j at time t; E min and E max The minimum and maximum power limits are respectively.

[0025] Furthermore, the spatiotemporal constraint of the mobile energy storage vehicle is expressed by the following formula:

[0026]

[0027]

[0028] Where: n is the set of mobile energy storage vehicles; D j,k (t) is the road distance between node j and node k; is the actual vehicle speed; and The travel time and installation time of the mobile energy storage vehicle between nodes j and k; Δt is the actual travel time of the mobile energy storage vehicle.

[0029] Furthermore, the charging and discharging constraints and energy constraints of the mobile energy storage vehicle are expressed by the following formula:

[0030]

[0031]

[0032]

[0033] Where: is the output of the electric bus at node j at time t; E EV The battery capacity of the electric bus; is the battery capacity consumed when traveling to node j; and are the initial SOC of the electric bus, the available SOC, the SOC used when going to node j, and the SOC reserved for the electric bus; R EV For all electric buses; c j EV It is a Boolean variable indicating whether the electric bus is heading to the node. A value of 1 indicates that the electric bus is heading to the node.

[0034] Furthermore, in S4, for more than two photovoltaic grid-connected points, if a mobile energy storage vehicle is able to go to multiple photovoltaic grid-connected points at the same time, one of the photovoltaic grid-connected points is randomly selected.

[0035] Beneficial effects:

[0036] The present invention utilizes mobile energy storage vehicles to absorb photovoltaic power from photovoltaic grid-connected points. At the same time, through the objective function, the mobile energy storage vehicles that absorb the most photovoltaic power from the photovoltaic grid-connected points and have the lowest energy consumption are screened out. This allows for reasonable utilization of energy while absorbing photovoltaic power from the photovoltaic grid-connected points. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a flow chart of this method.

[0038] Figure 2 This is the effect diagram of photovoltaic absorption of this method. DETAILED DESCRIPTION

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0040] like Figure 1 A mobile energy storage photovoltaic consumption method based on Dijkstra algorithm path planning includes the following steps:

[0041] S1: Mark all photovoltaic grid-connected nodes, then mark all mobile energy storage vehicle nodes, and determine the minimum traffic-grid coupling coefficient ω ij ;

[0042] The traffic-grid coupling coefficient is mainly composed of traffic influencing factors and grid influencing factors, where the traffic influencing factors are expressed by formula (1).

[0043] ωT=tij(1)

[0044] Where: t ij represents the travel time of the mobile energy storage vehicle from node i to node j; ω T is the traffic travel time, which is obtained from real-time traffic information and is a known quantity. Node i is considered to be the node where the mobile energy storage vehicle is located, and node j is considered to be the node where the photovoltaic grid is connected.

[0045] The grid influencing factors are expressed by formula (2).

[0046]

[0047] Where: is the photovoltaic power of the photovoltaic grid-connected point at node j at time t, which is a known quantity;

[0048] Traffic-grid coupling coefficient ω ijIt is formula (3).

[0049]

[0050] Where: μ 1 and μ 2 is the weight coefficient;

[0051] S2: Select ω ij The smallest node set Q of mobile energy storage vehicles;

[0052] For a photovoltaic grid-connected point at a certain node j, several mobile energy storage vehicles are needed to consume the energy. The nodes where the mobile energy storage vehicles are located are denoted as i1~i N . By formula (3), calculate i1~i N All mobile energy storage vehicles ij , calculated to be consistent with ω ij There may be more than one smallest mobile energy storage vehicle. ij Minimum and equal, update the nodes of these several mobile energy storage vehicles to the set Q.

[0053] S3: Establish a model for optimizing the dispatch of mobile energy storage vehicles;

[0054] S31: Establish objective function:

[0055]

[0056] Where: and is the weight coefficient; in is the photovoltaic surplus power at node j at time t; is the photovoltaic power at the photovoltaic grid-connected point j of the photovoltaic energy storage vehicle at time t.

[0057] S32: Energy constraints of mobile energy storage vehicles:

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065] Emin ≤E j.t ≤E max (12)

[0066] Where: j is the photovoltaic grid connection point and the node where each level of load is located; and are the charging and discharging active powers of the mobile energy storage vehicle at node j at time t; and are the reactive powers of charging and discharging of the mobile energy storage vehicle at node j at time t; is the maximum apparent power of the energy storage converter of the mth mobile energy storage vehicle; and are the charging and discharging flags of the mobile energy storage vehicle at node j at time t. If the mobile energy storage is in the charging state, then Otherwise 0; p chmax and p dismax are the maximum charging power and discharging power of the mobile energy storage vehicle respectively; and are the charging efficiency and discharging efficiency of the mobile energy storage vehicle at node j respectively; E j,t represents the amount of electricity of the mobile energy storage vehicle at node j at time t; E min and E max The minimum and maximum power limits are respectively.

[0067] S33: Spatiotemporal constraints of mobile energy storage vehicles:

[0068]

[0069]

[0070] Where: n is the set of mobile energy storage vehicles; D j,k (t) is the road distance between node j and node k; is the actual vehicle speed; and The travel time and installation time of the mobile energy storage vehicle between nodes j and k; Δt is the actual travel time of the mobile energy storage vehicle.

[0071] S34: Mobile energy storage vehicle charging and discharging constraints and energy constraints:

[0072] As a typical example of mobile energy storage vehicles, electric buses must meet the following charging and discharging constraints and energy limits:

[0073]

[0074]

[0075]

[0076] Where: is the output of the electric bus at node j at time t; E EV The battery capacity of the electric bus; is the battery capacity consumed when traveling to node j; and are the initial SOC of the electric bus, the available SOC, the SOC used when going to node j, and the SOC reserved for the electric bus; R EV For all electric buses; c j EV It is a Boolean variable indicating whether the electric bus is heading to the node. A value of 1 indicates that the electric bus is heading to the node.

[0077] S4: From the set Q of mobile energy storage vehicles in S2, by meeting the objective function in S3, the mobile energy storage vehicles to the photovoltaic grid connection point j are screened out from the set Q.

[0078] Specifically, the objective function in S3, as well as the corresponding energy constraints, spatiotemporal scheduling constraints, charge and discharge constraints, and energy constraints, further screen the mobile energy storage vehicles in the set Q in S2. All mobile energy storage vehicles in the set Q are calculated to determine whether they meet the objective function in S3. Finally, the mobile energy storage vehicles that meet the objective function are the mobile energy storage vehicles suitable for going to node j.

[0079] There may be more than one mobile energy storage vehicle that meets the objective function. Several mobile energy storage vehicles are updated to set Z1. For another PV grid connection point, another set of mobile energy storage vehicles, Z2, can be calculated using the aforementioned S1-S4. If a mobile energy storage vehicle meets the requirements for both PV grid connection points j1 and j2, one of these PV grid connection points is randomly selected.

[0080] In this method, first, a mobile energy storage vehicle is moved to the photovoltaic grid-connected point. The charging of the mobile energy storage vehicle will absorb the photovoltaic power of the photovoltaic grid-connected point. Secondly, the traffic-grid coupling coefficient is used to select a set Q of mobile energy storage vehicles that saves the most time and consumes the least energy to reach node j. The mobile energy storage vehicles in set Q are initially considered to be more suitable for going to node j where the photovoltaic grid is located. Finally, the objective function is used to further screen the mobile energy storage vehicles in set Q to select the mobile energy storage vehicle that can absorb the most photovoltaic grid-connected points.

[0081] Example 2:

[0082] A typical photovoltaic operation day was selected, and photovoltaic output information from 9 to 17 o'clock was imported. Two mobile energy storage vehicles with a capacity of 200kwh each were selected, with a charging and discharging efficiency of 0.9, and minimum and maximum SOC of 0.2 and 0.8 respectively. At 10 o'clock, they received the dispatch command and headed from their respective nodes to the photovoltaic grid-connected point to absorb photovoltaic power. The above-mentioned improved algorithm was used for the route to the point, and photovoltaic power was absorbed after arrival. The photovoltaic station used DC charging for the mobile energy storage, and the power was changed in a gradient from the initial 20KW to 90KW in the middle and then to the final 20KW. The absorption results are as follows: Figure 2 As shown, the total photovoltaic power absorbed is about 343.396KW.

[0083] The figure shows that during the peak period of photovoltaic power output (11:00 to 14:00), photovoltaic power was consumed in a timely manner, alleviating the storage pressure of fixed energy storage during the peak output period and realizing the "peak shaving" function; at the same time, mobile energy storage vehicles were charged to further provide electricity to various levels of loads that were short of power, realizing the "valley filling" function, thereby alleviating the pressure on electricity supply and realizing flexible electricity scheduling.

[0084] With the above-described preferred embodiments of the present invention as a guide, and with reference to the above description, relevant personnel are fully capable of making various changes and modifications without departing from the technical scope of this invention. The technical scope of this invention is not limited to the contents of the specification and must be determined according to the scope of the claims.

Claims

1. A mobile energy storage photovoltaic consumption method based on Dijkstra algorithm path planning, characterized in that: The following steps are included: S1: Mark all photovoltaic grid-connected nodes and the nodes where the mobile energy storage vehicles are located; calculate the minimum traffic-grid coupling coefficient ω ij ; Where: μ1 and μ2 are weight coefficients; ω T is the traffic travel time of the mobile energy storage vehicle from node i to node j, which is known from real-time traffic information; ω P is the photovoltaic power of the photovoltaic grid-connected point at node j, which is a known quantity; S2: ij The node where the smallest mobile energy storage vehicle is located is updated to the set Q; S3: Establish a model for optimizing the dispatch of mobile energy storage vehicles and screen the mobile energy storage vehicles in the set Q; the objective function of the model for optimizing the dispatch of mobile energy storage vehicles is: Where: and is the weight coefficient; in is the photovoltaic surplus power at node j at time t; is the photovoltaic power at the photovoltaic grid connection point j of the photovoltaic energy storage vehicle at time t; is the photovoltaic power at the photovoltaic grid-connected point j; The constraints include: energy constraints of mobile energy storage vehicles, time and space constraints of mobile energy storage vehicles, and charging and discharging constraints and energy constraints of mobile energy storage vehicles; S4: Bring the mobile energy storage vehicles in the set Q into S3 for calculation, and select the mobile energy storage vehicles to the photovoltaic grid connection point j; The energy constraint of the mobile energy storage vehicle is expressed by the following formula: AND min ≤E j.t ≤E max (10) Where: j is the photovoltaic grid-connected point and the node where each level of load is located; and are the charging and discharging active powers of the mobile energy storage vehicle at node j at time t respectively; and are the reactive power of charging and discharging of the mobile energy storage vehicle at node j at time t; is the maximum apparent power of the energy storage converter of the mth mobile energy storage vehicle; and are the charging and discharging flags of the mobile energy storage vehicle at node j at time t. If the mobile energy storage is in the charging state, then Otherwise, it is 0; p chmax and p dismax are the maximum charging power and discharging power of the mobile energy storage vehicle respectively; and are the charging efficiency and discharging efficiency of the mobile energy storage vehicle at node j respectively; E j,t represents the power of the mobile energy storage vehicle at node j at time t; E min and E max The minimum and maximum limits of the power respectively; The spatiotemporal constraint of the mobile energy storage vehicle is expressed by the following formula: Where: n is the set of mobile energy storage vehicles; D j,k (t) is the road distance between node j and node k; is the actual vehicle speed; and The travel time and installation time of the mobile energy storage vehicle between node j and node k; Δt is the actual travel time of the mobile energy storage vehicle; The charging and discharging constraints and energy constraints of mobile energy storage vehicles are expressed by the following formulas: Where: is the output of the electric bus at node j at time t; E EV The battery capacity of the electric bus; is the battery capacity consumed when traveling to node j; and are the initial SOC of the electric bus, the available SOC, the SOC used when going to node j, and the SOC reserved for the electric bus; R EV For all electric buses; c j EV It is a Boolean variable indicating whether the electric bus is heading to the node, and a value of 1 indicates heading to the node.

2. According to claim 1, a mobile energy storage photovoltaic consumption method based on Dijkstra algorithm path planning is characterized in that: In S4, for more than two photovoltaic grid-connected points, if a mobile energy storage vehicle can go to multiple photovoltaic grid-connected points at the same time, one of the photovoltaic grid-connected points is randomly selected.

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